---
title: "\\textsc{Managing the Costs of Backing Down}"
subtitle: "Strategies, Reputations, and Audience Costs in a Real-World Conflict"
author: "Appendices"
classoption: a4paper

header-includes:
  - \usepackage{float}
  - \usepackage[title]{appendix}
  - \usepackage{float}
  - \usepackage{xeCJK}
  - \usepackage{xeCJKfntef}
  - \usepackage{placeins}
  - \setCJKmainfont[Scale=1]{Source Han Serif}
  - \usepackage{kpfonts-otf}
  - \usepackage{caption}
  - \usepackage{setspace}
  - \captionsetup*{labelfont={bf},textfont={it},labelsep={colon},justification=centering,singlelinecheck=true}
  - \numberwithin{table}{section}
  
output: 
  bookdown::pdf_document2:
    latex_engine: xelatex
    number_sections: yes
    toc: yes
    toc_depth: 3
    fig_height: 4
    keep_tex: yes
    df_print: kable
---

```{r setup, include=FALSE}
library(tidyverse)
library(rstatix)
library(stargazer)
library(magrittr)
library(MASS)
library(kableExtra)
library(scales)
library(knitr)
library(DT)
library(showtext)
library(summarytools)

st_options(style = 'rmarkdown', headings = F)

theme_set(theme_bw()+
            theme(panel.grid.minor = element_blank(),
                  panel.grid.major = element_blank()))

knitr::opts_chunk$set(echo = F, warning = F, message = F, cache = T, comment = '', encoding = "UTF-8")

kable <- function(df, caption = tab.cap) {
  
  if ("p.signif" %in% names(df)) {
    hold <- NULL
    if("estimate" %in% names(df))
      df %<>% rename(`diff in means` = estimate)
    
    if ('estimate1' %in% names(df)) 
      df %<>% dplyr::select(!c(estimate1,estimate2))
    
    if ('conf.low' %in% names(df)) 
      df %<>% dplyr::select(!c(conf.low, conf.high, method, alternative))
    
    if ('.y.' %in% names(df))
      df %<>% dplyr::select(!`.y.`)
    
    if ("group1" %in% names(df)){ # two-sample $t$-tests
      hold = c(hold,"group1", 'group2', 'n1', 'n2')
      if("diff in means" %in% names(df))
        df %<>% mutate(
          `diff in means` = -`diff in means`,    # swap; group2 - group1
          statistic = -statistic
        )
    } else { # one-sample $t$-tests
      hold = c(hold,"n")
    }
    
    if("diff in means" %in% names(df))
      df %<>% relocate(`diff in means`, .before = statistic)
    
    #df %<>% df[ , c(hold, '`diff in means`', 'statistic', 'df', 'p', 'p.signif')]
    knitr::kable(df, booktabs = TRUE, digits = 3, 
               longtable = F, caption = tab.cap, format="latex",
               linesep = c(rep("",5), "\\addlinespace")) %>% 
    kable_styling(latex_options =c("scale_down", "HOLD_position"), position = "center") %>%
      add_footnote(label = "\\textit{Note: }$^{*}p<.05$; $^{**}p<.01$; $^{***}p<.001$; $^{****}p<.0001$", notation = "none", escape = F)
  } else if ("mad" %in% names(df)) {
    df  %>% dplyr::select(!c(min, max, iqr, mad)) %>% 
      knitr::kable(booktabs = TRUE, digits = 3, 
               longtable = F, caption = tab.cap, format="latex") %>% 
    kable_styling(latex_options =c("HOLD_position", "scale_down"), position = "center") 
  } else {
    knitr::kable(df, booktabs = TRUE, digits = 3, 
               longtable = F, caption = tab.cap, format="latex") %>% 
    kable_styling(latex_options = c("HOLD_position", "scale_down"), position = "center") 
  }
  
}

knit_print.data.frame <- function(x, ...) {
  res <- paste(c("", "", kable(x)), collapse = "\n")
  asis_output(res)
}

registerS3method("knit_print", "data.frame", knit_print.data.frame)

library(tidyverse)
library(rstatix)
library(magrittr)
library(MASS)
library(stargazer)
library(scales)

x_names_jp_threat <- c("1" = "control (C)",
             "2" = "C +\nUN\nmediation\noffer",
             "3" = "C +\nU.S.\nrescinds\nsupport",
             "4" = "C +\npeaceful\nidentity\nrhetoric",
             "5" = "C +\neconomic\ndevelopment\nrhetoric",
             "6" =  "C +\neconomic\nsanction",
             "7" = "C +\nno\nthreat"
)

x_names_cn_threat <- c("1" = "control (C)",
             "2" = "C +\nUN\nmediation\noffer",
             "3" = "C +\nU.S.\ndeterrent\nthreat",
             "4" = "C +\npeaceful\nidentity\nrhetoric",
             "5" = "C +\neconomic\ndevelopment\nrhetoric",
             "6" =  "C +\neconomic\nsanction",
             "7" = "C +\nno\nthreat"
)

x_names <- c("1" = "Control",
             "2" = "UN med",
             "3" = "US threat",
             "4" = "Peace id",
             "5" = "Econ dev",
             "6" =  "Sanction",
             "7" = "No threat"
)


# df1 <- read_csv("../audience_cost_rawdata.csv") %>% 
df1 <- read_csv("exp1.csv") %>% 
  mutate(
    experiment = "E1 Japan Threat",
    fig_lab = "E1: Japanese Leader",
    pat_name = x_names_jp_threat[pat]
  )

# df2 <- read_csv("../audiencecost_original_data.csv") %>% 
df2 <- read_csv("exp2.csv") %>% 
  mutate(
    experiment = "E2 China Threat",
    fig_lab = "E2: Chinese Leader",
    pat_name = x_names_cn_threat[pat]
  )


df <- rbind(df1, df2) %>%
  mutate(
    waiver = s00,
    yob = s01,
    sex = s02,
    
    age = 2020 - yob,
    
    # Module: Untying Hands
    tomz1 = q01,
    tomza = q02a,
    tomzd = q02b,
    tomzn = q02c,
    
    # Module: reputation
    dom_rep = q05,
    intl_rep = q06,
    
    # Demographics
    region = q08,
    edu = q09,
    income = q10, # Note: household income
    conservative = ifelse(as.numeric(q11) == 19, NA, as.numeric(q11)),
    cons = conservative >= 6,
    nativism = ifelse(as.numeric(q14) == 9, NA, 5 - as.numeric(q14)),
    nat = nativism >= 3,
    hawkishness = ifelse(as.numeric(q22) %in% 1:4, as.numeric(q22), NA),
    hawk = hawkishness >= 3,
    militarism = ifelse(as.numeric(q15) == 9, NA, as.numeric(q15)),
    mil = militarism >= 5,
    attention = ifelse(q07 < 7, q07 + 1, ifelse(q07 == 7, 1, NA)),
    att_cn = q23_1x,
    chinaphile = att_cn >= 51,
    att_us = q23_4x,
    us_phile = att_us >= 51,
    att_jp = q23_5x,
    japan_phile = att_jp >= 51,
    att_un = q23_7x,
    un_phile = att_un >= 51,
    egal_score = (q12_09 >= 5) + (q12_10 >= 5) +(q12_11 >= 5) + (q12_12 >= 5) + (q12_13 >= 5) + (q12_14 >= 5) + (q12_15 >= 5) + (q12_16 >= 5),
    egal = egal_score >= 5,
    tres_egal = egal_score >= 7
  ) %>% filter(
  # Waiver
  waiver == 1,
  # Age (Japanese adult age)
  age >= 20
)

### 7-point DV
df$approval <- NA
df$approval <- ifelse(df$tomz1 == 1, 
                      ifelse(!is.na(df$tomza), df$tomza + 6, NA),
                      df$approval)
df$approval <- ifelse(df$tomz1 == 0, 
                      ifelse(!is.na(df$tomzd), 2 - df$tomzd , NA),
                      df$approval)
df$approval <- ifelse(df$tomz1 == 9, 
                      ifelse(!is.na(df$tomzn), df$tomzn + 3, NA),
                      df$approval)

### Approval
df$approval_prop <- as.numeric(df$approval > 4)

```

\clearpage

\appendices

```{=tex}
\renewcommand\thetable{\Alph{section}\arabic{table}}
\renewcommand\thefigure{\Alph{section}\arabic{figure}}
```
# Survey Instruments

## Experiment 1: Japanese Threats

### IV: Treatments

ここからは、国際関係についてお伺いします。まず、将来起こる可能性のある国際危機のシナリオについてお読みいただき、その後で危機への対応についてのご意見をお尋ねします。

The following questions are about international affairs. We will describe an international crisis scenario that may occur in the future and ask for your assessment.

------------------------------------------------------------------------

**For all experimental groups:**

日本と中国は、ある領土の領有をめぐり長い間、争っています。最近になって中国が、争いのある領土に向かって軍事用ドローンを飛行させました。

China and Japan have a long-standing dispute over a piece of territory. Recently, China sent some military drones[^1] to the disputed territory.

[^1]: *Translation note*: may be one drone or several drones, but plural makes more sense in the context.

-   **`pat = 1`: Control.** 日本の首相は、もしドローンの飛行が続けば軍事行動をとる、と表明しました。中国は争いのある領土でのドローンの飛行を継続しました。結局、日本の首相は、中国に対して軍事行動をとらないことを決定しました。\
    The Japanese Prime Minister said that if China was to continue sending drones, Japan would take military action. China continued to send drones to the disputed territory. In the end, the Japanese PM decided not to take military action against China.
-   **`pat = 2`: UN mediation offer.** 日本の首相は、もしドローンの飛行が続けば軍事行動をとる、と表明しました。中国は争いのある領土でのドローンの飛行を継続しました。国際連合の事務総長は平和を呼びかけ、国連による仲介を申し出ました。結局、日本の首相は、中国に対して軍事行動をとらないことを決定しました。\
    The Japanese Prime Minister said that if China was to continue sending drones, Japan would take military action. China continued to send drones to the disputed territory. The UN Secretary-General called for peace and offered to mediate between the two countries. In the end, the Japanese PM decided not to take military action against China.
-   **`pat = 3`: US threat.** 日本の首相は、もしドローンの飛行が続けば軍事行動をとる、と表明しました。中国は争いのある領土でのドローンの飛行を継続しました。アメリカは日本が軍事行動をとった場合、米軍を派遣しないと警告しました。結局、日本の首相は、中国に対して軍事行動をとらないことを決定しました。\
    The Japanese Prime Minister said that if China was to continue sending drones, Japan would take military action against China. China continued to send drones to the disputed territory. The United States warned that it would not send troops to Japan if Japan attacks. In the end, the Japanese PM decided not to take military action against China.
-   **`pat = 4`: Peaceful identity rhetoric.** 日本の首相は、もしドローンの飛行が続けば軍事行動をとる、と表明しました。中国は争いのある領土でのドローンの飛行を継続しました。結局、日本の首相は、中国に対して軍事行動をとらないことを決定しました。そして日本の首相は、日本国民は武力を用いずに紛争を解決するために最大限の努力を行う、平和的な人びとであるとの声明を発表しました。\
    The Japanese Prime Minister said that if China was to continue sending drones, Japan would take military action. China continued to send drones to the disputed territory. In the end, the Japanese PM decided not to take military action against China. The Japanese PM declared that the Japanese people are peaceful and will strive for resolving conflicts without the use of force.
-   **`pat = 5`: Economic development rhetoric.** 日本の首相は、もしドローンの飛行が続けば軍事行動をとる、と表明しました。中国は争いのある領土でのドローンの飛行を継続しました。結局、日本の首相は、中国に対して軍事行動をとらないことを決定しました。そして日本の首相は、武力紛争は日本の経済的な発展と繁栄を妨げることになるだろうと説明しました。\
    The Japanese Prime Minister said that if China was to continue sending drones, Japan would take military action. China continued to send drones to the disputed territory. In the end, the Japanese PM decided not to take military action against China. The Japanese PM explained that a military conflict will derail Japan's economic development and prosperity.
-   **`pat = 6`: Economic sanction.** 日本の首相は、もしドローンの飛行が続けば軍事行動をとる、と表明しました。中国は争いのある領土でのドローンの飛行を継続しました。結局、日本の首相は、中国に対して軍事行動をとらないことを決定しましたが、中国経済に対する制裁として、輸入制限など一連の制裁措置を課しました。\
    The Japanese Prime Minister said that if China was to continue sending drones, Japan would take military action. China continued to send drones to the disputed territory. In the end, the Japanese PM decided not to take military action, but imposed a series of sanctions and boycotts on China.
-   **`pat = 7`: No threat.** 日本の首相は、これに介入するつもりはないと表明しました。中国は争いのある領土でのドローンの飛行を継続しました。結局、日本の首相は、中国に対して軍事行動をとらないことを決定しました。\
    The Japanese Prime Minister said that Japan would stay out of conflict. China continued to send drones to the disputed territory. In the end, the Japanese PM decided not to take military action.

### DV1: Approval score

**Q1:** あなたは、日本政府の対応を支持しますか、支持しませんか、それともどちらでもないですか。\
Do you approve or disapprove of the way the Japanese government handled the situation?^[In the Japanese context, referencing the *Seifu* (government) would be more appropriate and natural than pinpointing the prime minister in particular. As Japanese politics is overwhelmingly controlled by the prime minister's office (*Kantei*), the term *Seifu* (government) evokes the prime minister and his office rather than the entire Japanese executive branch (see e.g., Takenaka 2006). Indeed, there is a word *Seifu-yoto* (Government and the Ruling Party) in the common parlance, showing that people would typically think of the prime minister as the representative of both the government and the ruling party.  While the term *Seifu* (government) is used, Japanese people would commonly associate the term with the prime minister, the leader of the Japanese executive branch.]

-   支持する (Approve) [$\rightarrow$Q2A]
-   支持しない (Disapprove) [$\rightarrow$Q2B]
-   どちらとも言えない (Neither approve nor disapprove) [$\rightarrow$Q2C]

 

**Q2A:** 【支持すると回答した方にお伺いします。】あなたは、日本政府の対応を強く支持しますか、もしくは、どちらかといえば支持しますか。\
Do you approve strongly, or only somewhat?

-   強く支持する (Approve strongly) [`approval = 7`]
-   どちらかといえば支持する (Approve somewhat) [`approval = 6`]

 

**Q2B:** 【支持しないと回答した方にお伺いします。】あなたは、日本政府の対応を全く支持しませんか、もしくは、どちらかといえば支持しませんか。\
Do you completely disapprove, or only somewhat?

-   全く支持しない (Disapprove completely) [`approval = 1`]
-   どちらかといえば支持しない (Disapprove somewhat) [`approval = 2`]

 

**Q2C:** 【どちらとも言えないと回答した方にお伺いします。】あなたは、日本政府の対応をどちらかといえば支持しますか、もしくは、どちらかといえば支持しませんか、それともどちらでもないですか。\
Do you lean towards approving, lean towards disapproving, or do you lean neither way?

-   どちらかといえば支持する (Lean towards approving) [`approval = 5`]
-   どちらでもない (Lean neither way) [`approval = 4`]
-   どちらかといえば支持しない (Lean towards disapproving) [`approval = 3`]

 

**Q3x:** あなたは、日本政府の対応のどういった部分を支持しますか、もしくは支持しませんか。どのように感じられたかを教えて下さい。特になければ「特にない」とご記入ください。\
Please tell us why you approve or disapprove the way the Japanese government handled the situation.

-   [Open-ended]

### DV2: Reputation

**Q5:** 今回の事件で、日本の首相の\CJKunderline{国内社会における}評価はどのように変わったと思いますか。\
In your view, has the Japanese PM's reputation *within Japan* been improved or damaged by their handling of this incident?

-   評価が上がった (Improved)
-   評価がやや上がった (Improved by a little)
-   評価は変わらない (Neither improved nor damaged)
-   評価はやや下がった (Damaged by a little)
-   評価は下がった (Damaged)

 

**Q6:** 今回の事件で、日本の首相の\CJKunderline{国際社会における}評価はどのように変わったと思いますか。\
In your view, has the Japanese PM's reputation *in the international community* been improved or damaged by their handling of this incident?

-   評価が上がった (Improved)
-   評価がやや上がった (Improved by a little)
-   評価は変わらない (Neither improved nor damaged)
-   評価はやや下がった (Damaged by a little)
-   評価は下がった (Damaged)

### Causal mechanism and attention check

**Q4:** 以下の文章に関して、あなたの意見にもとも近いものを選んでください。\
Please tell us what you think about the following statements.

-   Statements:

    -   **Q4_1:** 首相はどんな時でも一度言ったことを撤回すべきでない。\
        The Japanese Prime Minister should not take back his words no matter what.
    -   **Q4_2:** 領土問題を解決するために軍事行動をとるべきではない。\
        Territorial disputes should not be addressed with military means.
    -   **Q4_3:** 首相は軍事行動をとって日本の領有権を明確にすべきだった。\
        It was appropriate for the Japanese government to affirm its sovereignty over the territory with military means.
    -   **Q4_4:** こうした事態の再発を懸念する。\
        Disputes like this are going to happen again.
    -   **Q4_5:** 中国の人々はもともと平和的な人々だ。\
        The Chinese people are peaceful.
    -   **Q4_6:** 日本の人々はもともと平和的な人々だ。\
        The Japanese people are peaceful.
    -   **Q4_7:** この問題では、どちらかといえば同意しないを選んでください。\
        Please choose 'disagree somewhat' for this question.

-   Choices:

    -   同意する (Agree)
    -   どちらかといえば同意する (Agree somewhat)
    -   どちらかといえば同意しない (Disagree somewhat)
    -   同意しない (Disagree)
    -   わからない (I don't know)

 

**Q7: Attention check.** あなたに先ほど読んでもらった日中関係に関する文章の中で、述べられていたものを一つ選んで答えてください。もしいずれも当てはまらない場合は、該当なし、を選んでください。\
Which of the following did you just read about?

-   国際連合が仲介を申し出た。\
    The United Nations offered mediation.
-   アメリカが米軍を派遣しないと表明した。\
    The US said it will not send troops.
-   日本の首相が、日本国民は平和的な人びとだと言った\
    The Japanese PM claimed that the Japanese people are peaceful.
-   中国への経済制裁が行われた。\
    Economic sanctions were to be imposed on China.
-   日本の首相は、この事案には介入しないと言った。\
    The Japanese PM said they would stay out of conflict.
-   日本の首相は、日本の経済的な発展に言及した。\
    The Japanese PM talked about economic development.
-   該当なし\
    None of the above.

## Experiment 2: Chinese Threats

### IV: Treatments

ここからは、国際関係についてお伺いします。まず、将来起こる可能性のある国際危機のシナリオについてお読みいただき、その後で危機への対応についてのご意見をお尋ねします。

The following questions are about international affairs. We will describe an international crisis scenario that may occur in the future and ask for your assessment.

------------------------------------------------------------------------

**For all experimental groups:**

日本と中国は、ある領土の領有をめぐり長い間、争っています。最近になって日本が、争いのある領土に構造物を建設し始めました。

China and Japan have a long-standing dispute over a piece of territory. Recently, Japan started to install structures on the disputed territory.

-   **`pat = 1`: Control.** 中国の政治指導部は、もし建設が続けば軍事行動をとる、と表明しました。日本は争いのある領土での構造物の建設を継続しました。結局、中国の政治指導部は、日本に対して軍事行動をとらないことを決定しました。\
    The Chinese leader said that if the installation continued, China would take military action. Japan continued to install structures on the disputed territory. In the end, the Chinese leader decided not to take military action against Japan.
-   **`pat = 2`: UN mediation offer.** 中国の政治指導部は、もし建設が続けば軍事行動をとる、と表明しました。日本は争いのある領土での構造物の建設を継続しました。国際連合の事務総長は平和を呼びかけ、国連による仲介を申し出ました。結局、中国の政治指導部は、日本に対して軍事行動をとらないことを決定しました。\
    The Chinese leader said that if the installation continued, China would take military action. Japan continued to install structures on the disputed territory. The UN Secretary-General called for peace and offered to mediate between the two countries. In the end, the Chinese leader decided not to take military action against Japan.
-   **`pat = 3`: US threat.** 中国の政治指導部は、もし建設が続けば軍事行動をとる、と表明しました。日本は争いのある領土での構造物の建設を継続しました。アメリカは、日本が攻撃された場合には軍事的に介入すると警告しました。結局、中国の政治指導部は、日本に対して軍事行動をとらないことを決定しました。\
    The Chinese leader said that if the installation continued, China would take military action. Japan continued to install structures on the disputed territory. The United States warned that it would intervene militarily if Japan is attacked. In the end, the Chinese leader decided not to take military action against Japan.
-   **`pat = 4`: Peaceful identity rhetoric.** 中国の政治指導部は、もし建設が続けば軍事行動をとる、と表明しました。日本は争いのある領土での構造物の建設を継続しました。結局、中国の政治指導部は、日本に対して軍事行動をとらないことを決定しました。そして中国の政治指導部は、中国人民は武力を用いずに紛争を解決するために最大限の努力を行う、平和的な人びとであるとの声明を発表しました。\
    The Chinese leader said that if the installation continued, China would take military action. Japan continued to install structures on the disputed territory. In the end, the Chinese leader decided not to take military action against Japan. The Chinese leader declared that the Chinese people are peaceful and will strive for resolving conflicts without the use of force.
-   **`pat = 5`: Economic development rhetoric.** 中国の政治指導部は、もし建設が続けば軍事行動をとる、と表明しました。日本は争いのある領土での構造物の建設を継続しました。結局、中国の政治指導部は、日本に対して軍事行動をとらないことを決定しました。そして中国の政治指導部は、武力紛争は中国の経済的な発展と繁栄を妨げることになるだろうと説明しました。\
    The Chinese leader said that if the installation continued, China would take military action. Japan continued to install structures on the disputed territory. In the end, the Chinese leader decided not to take military action against Japan. The Chinese leader explained that a military conflict will derail China's economic development and prosperity.
-   **`pat = 6`: Economic sanction.** 中国の政治指導部は、もし建設が続けば軍事行動をとる、と表明しました。日本は争いのある領土での構造物の建設を継続しました。結局、中国の政治指導部は、日本に対して軍事行動をとらないことを決定しましたが、日本経済に対する制裁として、輸入制限など一連の制裁措置を課しました。\
    The Chinese leader said that if the installation continued, China would take military action. Japan continued to install structures on the disputed territory. In the end, the Chinese leader decided not to take military action against Japan, but imposed a series of sanctions and boycotts on Japan.
-   **`pat = 7`: no threat.** 中国の政治指導部は、これに介入するつもりはないと表明しました。日本は争いのある領土での構造物の建設を継続しました。結局、中国の政治指導部は、日本に対して軍事行動をとらないことを決定しました。\
    The Chinese leader said that China would stay out of conflict. Japan continued to install structures on the disputed territory. In the end, the Chinese leader decided not to take military action against Japan

### DV1: Approval score

**Q1:** あなたは、日本政府の対応を支持しますか、支持しませんか、それともどちらでもないですか。\
Do you approve or disapprove of the way the Japanese government handled the situation?

-   支持する (Approve) [$\rightarrow$Q2A]
-   支持しない (Disapprove) [$\rightarrow$Q2B]
-   どちらとも言えない (Neither approve nor disapprove) [$\rightarrow$Q2C]

 

**Q2A:** 【支持すると回答した方にお伺いします。】あなたは、日本政府の対応を強く支持しますか、もしくは、どちらかといえば支持しますか。\
Do you approve strongly, or only somewhat?

-   強く支持する (Approve strongly) [`approval = 7`]
-   どちらかといえば支持する (Approve somewhat) [`approval = 6`]

 

**Q2B:** 【支持しないと回答した方にお伺いします。】あなたは、日本政府の対応を全く支持しませんか、もしくは、どちらかといえば支持しませんか。\
Do you completely disapprove, or only somewhat?

-   全く支持しない (Disapprove completely) [`approval = 1`]
-   どちらかといえば支持しない (Disapprove somewhat) [`approval = 2`]

 

**Q2C:** 【どちらとも言えないと回答した方にお伺いします。】あなたは、日本政府の対応をどちらかといえば支持しますか、もしくは、どちらかといえば支持しませんか、それともどちらでもないですか。\
Do you lean towards approving, lean towards disapproving, or do you lean neither way?

-   どちらかといえば支持する (Lean towards approving) [`approval = 5`]
-   どちらでもない (Lean neither way) [`approval = 4`]
-   どちらかといえば支持しない (Lean towards disapproving) [`approval = 3`]

 

**Q3:** あなたは、日本政府の対応のどういった部分を支持しますか、もしくは支持しませんか。どのように感じられたかを教えて下さい。特になければ「特にない」とご記入ください。\
Please tell us why you approve or disapprove the way the Japanese government handled the situation.

-   [Open-ended]

### DV2: Reputation

**Q5:** 今回の事件で、中国の政治指導部の\CJKunderline{中国国内における}評価はどのように変わったと思いますか。\
In your view, has the Chinese leadership's reputation *within China* been improved or damaged by their handling of this incident?

-   評価が上がった (Improved)
-   評価がやや上がった (Improved by a little)
-   評価は変わらない (Neither improved nor damaged)
-   評価はやや下がった (Damaged by a little)
-   評価は下がった (Damaged)

 

**Q6:** 今回の事件で、中国の政治指導部の\CJKunderline{国際社会における}評価はどのように変わったと思いますか。\
In your view, has the Chinese leadership's reputation *in the international community* been improved or damaged by their handling of this incident?

-   評価が上がった (Improved)
-   評価がやや上がった (Improved by a little)
-   評価は変わらない (Neither improved nor damaged)
-   評価はやや下がった (Damaged by a little)
-   評価は下がった (Damaged)

### Causal mechanism and attention check

**Q4:** 以下の文章に関して、あなたの意見にもとも近いものを選んでください。\
Please tell us what you think about the following statements.

-   Statements:

    -   **Q4_1:** 日本政府はそもそも建造物の建設を始めるべきでなかった。\
        The Japanese government should not have started the construction in the first place.
    -   **Q4_2:** 日本政府は途中で建造物の建設をやめるべきだった。\
        The Japanese government should have halted the construction.
    -   **Q4_3:** 日本政府の行動は日本の領有権を明確するための適切なものだった。\
        That was an appropriate means for the Japanese government to affirm its sovereignty over the territory.
    -   **Q4_4:** 日本政府が中国の脅しに屈しなかったのは正しかった。\
        The Japanese government was right not to bend to China's will.
    -   **Q4_5:** 中国の人々はもともと平和的な人々だ。\
        The Chinese people are peaceful.
    -   **Q4_6:** 日本の人々はもともと平和的な人々だ。\
        The Japanese people are peaceful.
    -   **Q4_7:** この問題では、どちらかといえば同意しないを選んでください。\
        Please choose 'disagree somewhat' for this question.

-   Choices:

    -   同意する (Agree)
    -   どちらかといえば同意する (Agree somewhat)
    -   どちらかといえば同意しない (Disagree somewhat)
    -   同意しない (Disagree)
    -   わからない (I don't know)

 

**Q7: Attention check.** あなたに先ほど読んでもらった日中関係に関する文章の中で、述べられていたものを一つ選んで答えてください。もしいずれも当てはまらない場合は、該当なし、を選んでください。\
Which of the following did you just read about?

-   国際連合が仲介を申し出た。\
    The United Nations offered mediation.
-   アメリカが介入すると表明する。\
    The US said it would intervene.
-   中国政治指導部が、中国国民は平和的な人々だと言った。\
    The Chinese leadership claimed that the Chinese people are peaceful.
-   日本への経済制裁が行われた。\
    Economic sanctions were to be imposed on Japan.
-   中国政治指導部が、この事案は介入しないと言った。\
    The Chinese leadership withheld from intervening.
-   中国の政治指導部は、中国の経済的な発展に言及した。\
    The Chinese leadership talked about China's economic development.
-   該当なし\
    None of the above.

## Shared Questions

### Political orientation and values

**Q11: Conservative-liberal spectrum.** 政治に関して、時々、「保守、革新（＝リベラル）」という表現をすることがあります。０が「革新」意味し、１０が「保守」を意味するとします。あなたはどこに位置すると思いますか。この中の番号でお答えください。\
On a scale from 0 (liberal) to 10 (conservative), where would you place yourself?

-   0--10 scale
-   答えたくない (I don't wish to disclose)

 

**Q12: Values.** 以下の文章を読んで、それぞれの主張にどれくらい同意するか、あるいは賛成するかを、1から7の数字を選んで答えてください。あまり深く考えないで、直感的に答えてください。\
Please tell us your first impression of the following statements on a 1 to 7 scale, where 1 means strongly disagree and 7 means strongly agree.

[1 (Strongly disagree)--7 (Strongly agree) Likert scale]

-   **Q12_01:** ある種の人たちは他の集団の人達よりも良い扱いを受けるに値する。\
    Some people deserve better treatment than others.
-   **Q12_02:** 自分たちが欲しい物を手に入れるためには、他の集団に対して力を振るわなければならないこともある。\
    Sometimes, one must use force to get what they want.
-   **Q12_03:** ある種の人たちが他の集団と比べて人生のチャンスに恵まれているとしても、それはそれで構わない。\
    It is fine that some people are born with more opportunities than others.
-   **Q12_04:** 人生で成功するためには、時として他の集団の人たちを踏み台にすることが必要だ。\
    Sometimes, it is necessary to use others for our success.
-   **Q12_05:** ある種の人たちの集団が身の程をわきまえていたら、世の中の色々な問題は起こらないで済むだろう。\
    If some people 'know their place', a lot of problems would not have happened.
-   **Q12_06:** ある種の人たちが上に立って、他の集団が下にいるのは、おそらく良いことだ。\
    It is perhaps a good thing that some people are at the top and some others are at the bottom.
-   **Q12_07:** 劣った人たちの集団は、自分たちの立場をわきまえるべきである。\
    People at the bottom should know their place.
-   **Q12_08:** 他の集団の人たちを現状に押し留めておくべき場合がある。\
    Sometimes, it is necessary for others to be kept in their place.
-   **Q12_09:** すべての集団が平等になれたら良い。\
    It's good when everyone is equal.
-   **Q12_10:** 私達は集団間の平等を理想とすべきだ。\
    We should strive for equality among social groups.
-   **Q12_11:** すべての人達の集団は人生のチャンスを等しく与えられるべきだ。\
    Everyone should be given equal opportunities.
-   **Q12_12:** 色々な集団が置かれた条件を等しくするために、私達はできるだけのことをすべきである。\
    We have an active duty to ensure that various groups are equally treated.
-   **Q12_13:** 私達は社会的平等を目指すべきである。\
    We should strive for social equality.
-   **Q12_14:** もし私達がいろんな集団をもっと平等に扱ってきたら、私達の問題はもっと少なくなるだろう。\
    We will have less problems if the people are treated more equally.
-   **Q12_15:** 私達は収入の平等をさらに目指すべきである。\
    We should strive for more equal incomes.
-   **Q12_16:** どんな集団も社会において支配的地位を独占するべきではない。\
    No group should dominate other groups.

　

**Q14: Nativism.** 次の文章についてあなたはどう感じますか。同意しますか。同意しませんか。「すべての国民は、どんな状況でも自分の国を支持するべきである。」\
Do you agree or disagree with the following statement? 'Everyone should support their own country no matter what.'

-   同意する (Agree)
-   どちらかといえば同意する (Agree somewhat)
-   どちらかといえば同意しない (Disagree somewhat)
-   同意しない (Disagree)
-   わからない (I don't know)

 

**Q15: Militarism.** 防衛費について、支出額を大幅に減らすぺきだと考える人がいます。そうした人の意見を以下の目盛りの「１」とします。反対に防衛費を大幅に増額すぺきだと考える人もいます。こちら側の人の意見を目盛りの「７」とします。もちろん、これらの中間の意見を持つ人もいます。そのような目盛りを用いたときに、あなたは防衛費について、どのような意見を持っていますか。\
If '1' means that you believe military spending should decrease significantly, and '7' means that you believe military spending should increase significantly, where would you put yourself from 1 to 7?

-   1 (政府は防衛費を削減すべきだ。The government should decrease military spending significantly) to 7 (政府は防衛費を増額すべきだ。The government should increase military spending significantly); 7-pt Likert scale; or
-   わからない (I don't know)

 

**Q22: Hawkishness.** あなたは、ご自身をタカ派だと考えますか。または、ハト派だと考えますか。ここでタカ派とは、自国の安全のために度々軍事力の行使が必要だと考える人のことを指します。ハト派とは、軍事力は決して、または、ほぼ行使してはいけないものと考える人のことを指します。\
Do you think that you are hawkish or dovish? To be 'hawkish' means that you believe your country should exercise its military power frequently to protect its security; to be 'dovish' means that you believe your country should never, or almost never, exercise its military power.

-   同意する (Agree)
-   どちらかといえば同意する (Agree somewhat)
-   どちらかといえば同意しない (Disagree somewhat)
-   同意しない (Disagree)
-   わからない (I don't know)

 

**Q23: Attitude towards other countries.** ある国・地域や国際組織に対する好感度をお尋ねします。ここでは、まったく好感を抱いていない状態を「0」、とても好感を抱いている状態を「100」とそれぞれ定着するとします。そのような目盛りを用いたときに、次にあげる国々に対して、あなたはどのくらいの好感を抱いていますか。\
Please rate the following countries or organisations on a scale from 0 to 100, where '0' means you feel very cold or unfavorable towards them and '100' means you feel very warm or favorable.

-   中国 China
-   北朝鮮 North Korea
-   韓国 South Korea
-   アメリカ The US
-   日本 Japan
-   台湾 Taiwan
-   国際連合 The UN

### Demographics

**Q8: Region.** あなたが居住しているのは、どの都道府県ですか。\
Which prefecture are you currently living in?

**Q9: Education.** あなたが最後に卒業した、あるいは現在在学中の学校（すなわち、あなたの最終学歴）、次のうちどれでしょうか。\
What is the highest level of education you have completed or are completing?

-   小学校・中学校卒業または高校在学中・中退\
    Primary or middle school graduate / Currently in high school / High school dropout
-   高校卒業\
    High school graduate
-   大学在学中・中退\
    Currently in college / College dropout
-   大学卒業\
    College graduate
-   大学院在学中・中退\
    Currently in graduate school / Graduate school dropout
-   大学院卒業\
    Postgraduate qualification
-   答えたくない\
    I don't wish to disclose

**Q10: Income.** あなたの世帯における1年間の税込みの総収入は、以下の選択肢のうち、どれに一番近いでしょうか。\
Please tell us your pre-tax household income.

-   ３５０万円未満\
    $x <$ 3.5 million yen
-   ３５０万円～４８０万円未満\
    3.5 million yen $\leq x <$ 4.8 million yen
-   ４８０万円～６３０万円未満\
    4.8 million yen $\leq x <$ 6.3 million yen
-   ６３０万円～８３０万円未満\
    6.3 million yen $\leq x <$ 8.3 million yen
-   ８３０万円以上\
    $x \geq$ 8.3 million yen
-   わからない\
    I don't know

**Q13: Choice of media.** あなたは、以下のメディアを通じて、日本\CJKunderline{以外}の国についてのニュースをどのニュースをどのくらいみたり読んだりしますか。\
How often do you read about countries *other than Japan* on the following platforms?

-   テレビニュース\
    Television
-   紙の新聞\
    Newspaper
-   TwitterなどのSNS\
    Social media (such as Twitter)
-   ５ちゃんねる（２ちゃんねる）やまとめサイト\
    5ch, 2ch, or *matome* blogs

【全くない・週に１～２日・週に３～４日・週に５～６日・毎日またはほぼ毎日・答えたくない】\
[Never / one or two days a week / three or four days a week / five to six days a week / every day or almost every day / I don't wish to disclose]

**Q16: Religion.** あなたは、日ごろから特定の宗教を信仰していますか。信仰していませんか。\
Are you religious?

-   信仰する宗教はない\
    No
-   信仰する宗教がある\
    Yes
-   答えたくない\
    I don't wish to disclose

**Q17--21: Political knowledge:** 以下の質問項目について、正しいと思うものを選択してください。\
For the following questions, please choose the option that you think is correct.

これまでの質問とは異なり、正解が存在しますが、これらの項目が社会の中でどれだけ知られているかを調べるのものだ、個人ごとの正解数を競うものではありません。正解数が報酬のお支払に影響することもありません。解答がわからない場合は調べたりせずに、「わからない」とお答えください。\
Although there are correct answers for the questions below, we are only interested in knowing how popular these facts are among the public---the number of correct answers have no effect on your reward. If you don't know the answer, please do not look up the answer and choose 'I don't know'.

**Q17:** 日本で、内閣総理大臣（首相）になるために必要な条件は、以下のうちどれでしょう。\
Which of the following is a prerequisite of becoming the Prime Minister of Japan?

-   衆議院議員であること\
    One is a member of the House of Representatives (*Shūgiin*)
-   参議院議員であること\
    One is a member of the House of Councillors (*Sangiin*)
-   国会議員であれば衆議院議員でも参議院議員でも良い\
    It doesn't matter whether one is as a Councillor or a Representative as long as he/she is a member of the National Diet
-   国会議員でなくともなれる\
    One can become a prime minister even if he is not a member of the National Diet.
-   わからない\
    I don't know

**Q18:** 衆議院で可決された法案が参議院で否決された場合、衆議院で再審議されることになります。衆議院で再び可決して、法案を成立させるためには、衆議院の出席議員のうち、どのくらいの割合の賛成が必要になるでしょう。\
If a bill that has been passed by the House of Representative is vetoed by the House of Councillors, how many votes among the present members of the House of Representatives are required to override the veto?

-   ３分の１\
    One third
-   過半数\
    More than half
-   ３分の２\
    Two thirds
-   ４分の３\
    Three quarters
-   わからない\
    I don't know

**Q19:** 裁判における判決に不服のある人は、上級の裁判所に改めて訴えを起こすことが認められていますが、日本では現在、最大何回まで裁判が受けられるでしょう。\
Citizens have the right to appeal against a court judgment. At most, how many times can a case be heard by a court?

-   ２回\
    Twice
-   ３回\
    Three times
-   ４回\
    Four times
-   ５回\
    Five times
-   わからない\
    I don't know

**Q20:** 日本国憲法で戦争放棄条項を含むのは何条でしょう。\
Which article of the Japanese constitution renounces Japan's right of belligerency?

-   第１条\
    Article 1
-   第９条\
    Article 9
-   第１７条\
    Article 17
-   第２５条\
    Article 25
-   わからない\
    I don't know

**Q21:** 日本国憲法で国権の最高機関として位置付けられているのは次のどれでしょう。\
Which of the following is the highest organ of state power under the Japanese Constitution?

-   内閣\
    The Cabinet
-   国会\
    The National Diet
-   最高裁判所\
    The Supreme Court of Japan
-   天皇\
    The Japanese Emperor
-   わからない\
    I don't know

\clearpage

# Sampling and Administrative Details

The experiments were fielded from January 24th to 28th 2020 by Nikkei Research Inc (<https://www.nikkei-r.co.jp/english/>) using a quota sampling approach. Nikkei Research is a nationally well-known survey company and its "Nikkei Research Access Panel" holds approximately 150,000 people, together with collaborative panels of around 6 million people residing in Japan. Quotas are calculated based on the gender, age cohort, and the six regions of Japan, namely, Hokkaido-Tohoku, Kanto, Chubu, Kansai, Shikoku-Chugoku, Kyushu-Okinawa. 1,524 Japanese adults were recruited for Experiment 1 (where Japan threatens and later backs down) and 1,493 adults were recruited for Experiment 2 (where it was China that threatens and later backs down). The samples were recruited within the Japanese adult age range from 18 to 69, the distribution of which is presented in the table below.

```{=tex}
\begin{table}[!htbp]
\caption{Targeted sample quota for gender, age cohort, and location of residence}
\resizebox{\textwidth}{!}{%
\begin{tabular}{@{}llllllllllll@{}}
\toprule
                & \multicolumn{5}{c}{Male}                 & \multicolumn{5}{c}{Female}               &        \\ \cmidrule(lr){2-6}\cmidrule(lr){7-11}
                & 20s   & 30s    & 40s    & 50s   & 60s    & 20s   & 30s    & 40s    & 50s   & 60s    & Total  \\ \midrule
Hokkaido Tohoku & 0.9\% & 1.1\%  & 1.2\%  & 1.2\% & 1.3\%  & 0.8\% & 1.1\%  & 1.2\%  & 1.2\% & 1.4\%  & 11.4\% \\
Kanto           & 2.9\% & 3.9\%  & 4.2\%  & 1.2\% & 2.5\%  & 2.7\% & 3.6\%  & 3.9\%  & 3.0\% & 3.6\%  & 34.4\% \\
Chubu           & 1.5\% & 1.9\%  & 2.1\%  & 1.7\% & 2.0\%  & 1.4\% & 1.8\%  & 2.0\%  & 1.7\% & 2.1\%  & 18.1\% \\
Kansai          & 1.3\% & 1.6\%  & 1.9\%  & 1.4\% & 1.8\%  & 1.3\% & 1.7\%  & 1.9\%  & 1.5\% & 1.9\%  & 16.2\% \\
Chugoku Shikoku & 0.7\% & 0.9\%  & 0.9\%  & 0.8\% & 1.1\%  & 1.7\% & 0.8\%  & 0.9\%  & 0.9\% & 1.1\%  & 8.7\%  \\
Kyushu Okinawa  & 0.9\% & 1.1\%  & 1.1\%  & 1.1\% & 1.3\%  & 1.9\% & 1.1\%  & 1.2\%  & 1.2\% & 1.3\%  & 11.2\% \\ \midrule
Total           & 8.2\% & 10.5\% & 11.3\% & 9.5\% & 10.9\% & 7.8\% & 10.0\% & 11.0\% & 9.4\% & 11.5\% & 100\%  \\ \bottomrule
\end{tabular}%
}
\end{table}
```
There are a number of practical elements in the administration of our study in addition to what was described in the main text. First, participants were offered gift points (called "dot money") for completing the survey. The amount of the points varies (Nikkei Research allocated the amount of the points randomly, weighted by the respondent's participation rate across different surveys). The dot money can be exchanged into Amazon Gift Cards and mileage points of major Japanese airlines like JAL and ANA. On average, survey participants can expect about 50 to 500 yen equivalent points from participating in the study. Second, Nikkei Research generated the survey web screens on their own platform (i.e. the company did not allow us to use our own survey software such as Qualtrics). Third, our survey was advertised with a neutral title "国際問題に関するアンケート (Survey on International Affairs)" rather than a specific title such as China-Japan territorial conflict. This avoids priming the respondents or creating unintended self-selection effects. The proportion of participants who responded to the invitation to participate is similar across the two experiments: 8.7% (1,524/17,500) in Experiment 1 and 8.5% (1,493/17,500) in Experiment 2. Participants in one experiment were unable to participate in the other experiment.

\clearpage

# Heterogenous Treatment Effects

We have conducted a series of additional analyses to test for heterogeneous treatment effects. The following section presents subsets according to political orientation, values, nativism, militarism, hawkishness, and attitudes towards certain countries and international organisations. In sum, we found that (i) conservatives are the main drivers behind the principal outcome as to the approval of the leader, (ii) tendencies for egalitarianism, nativism, militarism and hawkishness do not consistently affect the reported outcome, and (iii) there is no heterogeneous treatment effect for attitudes towards other countries, such as China and the US.

The statistics are laid out in full below:

## Subset: Political Orientation

Respondents are coded as conservative (`cons` = 1) if their self-assigned conservativeness score is greater than or equal to 6 (out of 10), 0 if otherwise.

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by conservativeness"

df %>% filter(!is.na(cons)) %>% group_by(cons, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by conservativeness"

df %>% filter(!is.na(cons)) %>% group_by(cons, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by conservativeness"

df %>% filter(!is.na(cons)) %>% group_by(cons, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by conservativeness"

df %>% filter(!is.na(cons)) %>% group_by(cons, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

## Subset: Values

Respondents are coded as egalitarians (`egal` = 1) if they agree with the 5 or more of the following statements; hardcore egalitarians (`tres_egal` = 1) if they agree with 7 or more.

-   **Q12_09:** すべての集団が平等になれたら良い。\
    It's good when everyone is equal.
-   **Q12_10:** 私達は集団間の平等を理想とすべきだ。\
    We should strive for equality among social groups.
-   **Q12_11:** すべての人達の集団は人生のチャンスを等しく与えられるべきだ。\
    Everyone should be given equal opportunities.
-   **Q12_12:** 色々な集団が置かれた条件を等しくするために、私達はできるだけのことをすべきである。\
    We have an active duty to ensure that various groups are equally treated.
-   **Q12_13:** 私達は社会的平等を目指すべきである。\
    We should strive for social equality.
-   **Q12_14:** もし私達がいろんな集団をもっと平等に扱ってきたら、私達の問題はもっと少なくなるだろう。\
    We will have less problems if the people are treated more equally.
-   **Q12_15:** 私達は収入の平等をさらに目指すべきである。\
    We should strive for more equal incomes.
-   **Q12_16:** どんな集団も社会において支配的地位を独占するべきではない。\
    No group should dominate other groups.

### Egalitarianism

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by egalitarianism"

df %>% filter(!is.na(egal)) %>% group_by(egal, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by egalitarianism"

df %>% filter(!is.na(egal)) %>% group_by(egal, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by egalitarianism"

df %>% filter(!is.na(egal)) %>% group_by(egal, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by egalitarianism"

df %>% filter(!is.na(egal)) %>% group_by(egal, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

### Hardcore egalitarianism

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by hardcore egalitarianism"

df %>% filter(!is.na(tres_egal)) %>% group_by(tres_egal, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by hardcore egalitarianism"

df %>% filter(!is.na(tres_egal)) %>% group_by(tres_egal, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by hardcore egalitarianism"

df %>% filter(!is.na(tres_egal)) %>% group_by(tres_egal, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by hardcore egalitarianism"

df %>% filter(!is.na(tres_egal)) %>% group_by(tres_egal, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

## Subset: Nativism

Respondents are coded as nativists (`nat = 1`) if they somewhat agree or strongly agree that 'every national should support their own country no matter what'.

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by nativism"

df %>% filter(!is.na(nat)) %>% group_by(nat, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by nativism"

df %>% filter(!is.na(nat)) %>% group_by(nat, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by nativism"

df %>% filter(!is.na(nat)) %>% group_by(nat, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by nativism"

df %>% filter(!is.na(nat)) %>% group_by(nat, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

## Subset: Militarism

Respondents are coded as militarists (`mil` = 1) if they score themselves 5 or above on a scale from 1 (government should decrease military spending significantly) to 7 (government should increase military spending significantly).

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by militarism"

df %>% filter(!is.na(mil)) %>% group_by(mil, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by militarism"

df %>% filter(!is.na(mil)) %>% group_by(mil, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by militarism"

df %>% filter(!is.na(mil)) %>% group_by(mil, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by militarism"

df %>% filter(!is.na(mil)) %>% group_by(mil, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

## Subset: Hawkishness

Respondents are coded as hawks (`hawk` = 1) if they agree or agree somewhat that their country should exercise their military power frequently to protect their security.

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by hawkishness"

df %>% filter(!is.na(hawk)) %>% group_by(hawk, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by hawkishness"

df %>% filter(!is.na(hawk)) %>% group_by(hawk, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by hawkishness"

df %>% filter(!is.na(hawk)) %>% group_by(hawk, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by hawkishness"

df %>% filter(!is.na(hawk)) %>% group_by(hawk, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

## Subset: Attitudes towards other countries

For the purpose of this section, respondents are considered "warm" towards a country if they assign a score of 51 or above in a 0 to 100 feeling thermometer.

### China

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by attitude towards China"

df %>% filter(!is.na(chinaphile)) %>% group_by(chinaphile, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by attitude towards China"

df %>% filter(!is.na(chinaphile)) %>% group_by(chinaphile, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by attitude towards China"

df %>% filter(!is.na(chinaphile)) %>% group_by(chinaphile, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by attitude towards China"

df %>% filter(!is.na(chinaphile)) %>% group_by(chinaphile, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

### The US

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by attitude towards the US"

df %>% filter(!is.na(us_phile)) %>% group_by(us_phile, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by attitude towards the US"

df %>% filter(!is.na(us_phile)) %>% group_by(us_phile, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by attitude towards the US"

df %>% filter(!is.na(us_phile)) %>% group_by(us_phile, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by attitude towards the US"

df %>% filter(!is.na(us_phile)) %>% group_by(us_phile, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

### Japan

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by attitude towards Japan"

df %>% filter(!is.na(japan_phile)) %>% group_by(japan_phile, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by attitude towards Japan"

df %>% filter(!is.na(japan_phile)) %>% group_by(japan_phile, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by attitude towards Japan"

df %>% filter(!is.na(japan_phile)) %>% group_by(japan_phile, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by attitude towards Japan"

df %>% filter(!is.na(japan_phile)) %>% group_by(japan_phile, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```

### The United Nations

```{r}

tab.cap = "$t$-tests on 7-pt approval score; subset by attitude towards the UN"

df %>% filter(!is.na(un_phile)) %>% group_by(un_phile, experiment) %>%
  t_test(detailed = T, approval ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on percentage approval; subset by attitude towards the UN"

df %>% filter(!is.na(un_phile)) %>% group_by(un_phile, experiment) %>%
  t_test(detailed = T, approval_prop ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt domestic reputation score; subset by attitude towards the UN"

df %>% filter(!is.na(un_phile)) %>% group_by(un_phile, experiment) %>%
  t_test(detailed = T, dom_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")

tab.cap = "$t$-tests on 5-pt international reputation score; subset by attitude towards the UN"

df %>% filter(!is.na(un_phile)) %>% group_by(un_phile, experiment) %>%
  t_test(detailed = T, intl_rep ~ pat, p.adj = "none") %>% 
  filter(group1 == 1) %>% mutate(group1 = x_names[group1], group2 = x_names[group2]) %>%
  dplyr::select(!c(p.adj, p.adj.signif)) %>%
  add_significance("p")
```


\clearpage

# Text Analysis on Open Responses

This section presents a supplementary analysis on the open-ended questions concerning the reasons behind the respondents' approval or disapproval of the Japanese government.

To disaggregate the mechanisms that drive these responses, we adopt the approach in Levy et al. (2015) and zero in on potential concerns about (i) reputation, (ii) credibility, and (iii) competence. The keywords associated with each mechanism are laid out in the ensuing table.

\vspace{1em}

| Mechanism   | Keywords         |
|-------------|------------------|
| Reputation  | 名誉 (honor and fame)、評判 (reputation)、評価 (reputation)|
| Credibility | 信用 (credibility)、信頼 (believe in)、信憑 (trust) |
| Competence  | 弱腰 (weak-kneed)、負け (lose)、譲歩 (back down)|

: Selected keywords for text analysis

\vspace{2em}

In sum, the keyword analysis shows that only responses that *approve* of the Japanese government in experiment 2 (where China threatened and backed down) are clearly attributable to concerns over competence. But the effects of the three mechanisms are much more subdued among respondents who *disapprove* of the Japanese government in experiment 2. For experiment 1 (where Japan threatened and backed down), concerns over reputation seem to be shared by all respondents regardless of whether they approve or disapprove of the Japanese government; while those who *disapproved* in experiment 1 have additionally cited *competence* as one of their major concerns.

<!-- We also trained several structured topic models (STMs) for each subgroup to explore the potential alternative mechanisms. We find that among respondents who *disapprove* of the Japanese government in experiment 1, themes such as "following through (実行)", "armed conflict (武力紛争)", "escalation (エスカレート)" are recurring. The detailed results are presented below: -->

\vspace{2em}

```{r, include = F}
# df %<>% mutate(
#   reputation_cnt = (q03x %>% str_count("名誉|評判|評価")),
#   credibility_cnt = (q03x %>% str_count("信用|信頼|信憑")),
#   competence_cnt = (q03x %>% str_count("弱腰|負け|譲歩")),
#   valid = (q03x %in% c("特にない", "特になし", "特に無い") == FALSE)
# )
# 
# sum_freq <- df %>%
#   group_by(experiment, approval_prop) %>%
#   summarise(
#     reputation = sum(reputation_cnt, na.rm = T),
#     credibility = sum(credibility_cnt, na.rm = T),
#     competence = sum(competence_cnt, na.rm = T)
#   ) %>% gather("Concern", "Freq", 3:5) %>%
#   mutate(
#     Approval = case_when(
#     approval_prop == 0 ~ "Those who disapprove",
#     approval_prop == 1 ~ "Those who approve"
#   ),
#   Concern = factor(Concern, levels = c("reputation", "credibility", "competence"))
# 
# )
# 
# ggplot(sum_freq, aes(Concern, Freq, fill = Concern)) +
#   geom_bar(stat = "identity") +
#   #scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
#   labs(
#     x = "Keywords for concern over...",
#     y = "Frequency",
#   ) + facet_grid(Approval ~ experiment) +
#   theme(
#     legend.position = "none"
#   )
#  
# ggsave("fig/figd1.pdf", width = 6, height = 4)

```

```{r, fig.cap = "Frequency of selected keywords"}
knitr::include_graphics("figd1.pdf")
```

<!-- ```{r, fig.cap = "Selected topics for open responses in experiment 1, subset by respondents who approve of the Japanese government", dev = "png"} -->

<!-- library(tm) -->

<!-- library(stm) -->

<!-- library(stopwords) -->

<!-- library(quanteda) -->

<!-- library(tidyverse) -->

<!-- library(readtext) -->

<!-- library(stringr) -->

<!-- library(dplyr) -->

<!-- library(lubridate) -->

<!-- library(topicmodels) -->

<!-- set.seed(100) -->

<!-- # data1 <- df %>% filter(experiment == "E1 Japan Threat" & approval_prop == 1) -->

<!-- #  -->

<!-- # full_corp1 <- corpus(data1, text_field = "q03x") -->

<!-- #  -->

<!-- # toks1 <- tokens(full_corp1, remove_punct = FALSE) -->

<!-- # toks1 <- tokens_select(toks1, '^[０-９ぁ-んァ-ヶー一-龠]+$', valuetype = 'regex', padding = TRUE) -->

<!-- # toks1 <- tokens_remove(toks1, '^[ぁ-ん]+$', valuetype = 'regex', padding = TRUE) -->

<!-- #  -->

<!-- # mx <- dfm(toks1) -->

<!-- # mx <- dfm_remove(mx, '特に') -->

<!-- # mx <- dfm_remove(mx, '思う') -->

<!-- # mx <- dfm_remove(mx, '的') -->

<!-- # mx <- dfm_remove(mx, '点') -->

<!-- #  -->

<!-- # open_dfm1 <- dfm(toks1, remove = "") %>% -->

<!-- #     dfm_remove("^[ぁ-ん]+$", valuetype = "regex", min_nchar = 2) %>% -->

<!-- #     dfm_trim(min_termfreq = 0.50, termfreq_type = "quantile", max_termfreq = 0.99) -->

<!-- #  -->

<!-- # stm_doc <- convert(open_dfm1, to = "stm") -->

<!-- #  -->

<!-- # model <- stm(stm_doc$documents, stm_doc$vocab, K = 10) -->

<!-- # save(model, file = "models/exp1_app") -->

<!-- load("models/exp1_app") -->

<!-- plot.STM(model, type = "summary", main = "Top Topics: Experiment 1, Respondents who approve") -->

<!-- ``` -->

<!-- ```{r fig.cap = "Selected topics for open responses in experiment 1, subset by respondents who disapprove of the Japanese government", dev = "png"} -->

<!-- # data1 <- df %>% filter(experiment == "E1 Japan Threat" & approval_prop == 0) -->

<!-- #  -->

<!-- # full_corp1 <- corpus(data1, text_field = "q03x") -->

<!-- #  -->

<!-- # toks1 <- tokens(full_corp1, remove_punct = FALSE) -->

<!-- # toks1 <- tokens_select(toks1, '^[０-９ぁ-んァ-ヶー一-龠]+$', valuetype = 'regex', padding = TRUE) -->

<!-- # toks1 <- tokens_remove(toks1, '^[ぁ-ん]+$', valuetype = 'regex', padding = TRUE) -->

<!-- #  -->

<!-- # mx <- dfm(toks1) -->

<!-- # mx <- dfm_remove(mx, '特に') -->

<!-- # mx <- dfm_remove(mx, '思う') -->

<!-- # mx <- dfm_remove(mx, '的') -->

<!-- # mx <- dfm_remove(mx, '点') -->

<!-- #  -->

<!-- # open_dfm1 <- dfm(toks1, remove = "") %>% -->

<!-- #     dfm_remove("^[ぁ-ん]+$", valuetype = "regex", min_nchar = 2) %>% -->

<!-- #     dfm_trim(min_termfreq = 0.50, termfreq_type = "quantile", max_termfreq = 0.99) -->

<!-- #  -->

<!-- # stm_doc <- convert(open_dfm1, to = "stm") -->

<!-- # model <- stm(stm_doc$documents, stm_doc$vocab, K = 10) -->

<!-- # save(model, file = "models/exp1_disapp") -->

<!-- load("models/exp1_disapp") -->

<!-- plot.STM(model, type = "summary", main = "Top Topics: Experiment 1, Respondents who disapprove") -->

<!-- ``` -->

<!-- ```{r fig.cap = "Selected topics for open responses in experiment 2, subset by respondents who approve of the Japanese government", dev = "png"} -->

<!-- # data1 <- df %>% filter(experiment == "E2 China Threat" & approval_prop == 1) -->

<!-- #  -->

<!-- # full_corp1 <- corpus(data1, text_field = "q03x") -->

<!-- #  -->

<!-- # toks1 <- tokens(full_corp1, remove_punct = FALSE) -->

<!-- # toks1 <- tokens_select(toks1, '^[０-９ぁ-んァ-ヶー一-龠]+$', valuetype = 'regex', padding = TRUE) -->

<!-- # toks1 <- tokens_remove(toks1, '^[ぁ-ん]+$', valuetype = 'regex', padding = TRUE) -->

<!-- #  -->

<!-- # mx <- dfm(toks1) -->

<!-- # mx <- dfm_remove(mx, '特に') -->

<!-- # mx <- dfm_remove(mx, '思う') -->

<!-- # mx <- dfm_remove(mx, '的') -->

<!-- # mx <- dfm_remove(mx, '点') -->

<!-- #  -->

<!-- # open_dfm1 <- dfm(toks1, remove = "") %>% -->

<!-- #     dfm_remove("^[ぁ-ん]+$", valuetype = "regex", min_nchar = 2) %>% -->

<!-- #     dfm_trim(min_termfreq = 0.50, termfreq_type = "quantile", max_termfreq = 0.99) -->

<!-- #  -->

<!-- # stm_doc <- convert(open_dfm1, to = "stm") -->

<!-- # model <- stm(stm_doc$documents, stm_doc$vocab, K = 10) -->

<!-- # save(model, file = "models/exp2_app") -->

<!-- load("models/exp2_app") -->

<!-- plot.STM(model, type = "summary", main = "Top Topics: Experiment 2, Respondents who approve") -->

<!-- ``` -->

<!-- ```{r fig.cap = "Selected topics for open responses in experiment 2, subset by respondents who disapprove of the Japanese government", dev = "png"} -->

<!-- # data1 <- df %>% filter(experiment == "E2 China Threat" & approval_prop == 0) -->

<!-- #  -->

<!-- # full_corp1 <- corpus(data1, text_field = "q03x") -->

<!-- #  -->

<!-- # toks1 <- tokens(full_corp1, remove_punct = FALSE) -->

<!-- # toks1 <- tokens_select(toks1, '^[０-９ぁ-んァ-ヶー一-龠]+$', valuetype = 'regex', padding = TRUE) -->

<!-- # toks1 <- tokens_remove(toks1, '^[ぁ-ん]+$', valuetype = 'regex', padding = TRUE) -->

<!-- #  -->

<!-- # mx <- dfm(toks1) -->

<!-- # mx <- dfm_remove(mx, '特に') -->

<!-- # mx <- dfm_remove(mx, '思う') -->

<!-- # mx <- dfm_remove(mx, '的') -->

<!-- # mx <- dfm_remove(mx, '点') -->

<!-- #  -->

<!-- # open_dfm1 <- dfm(toks1, remove = "") %>% -->

<!-- #     dfm_remove("^[ぁ-ん]+$", valuetype = "regex", min_nchar = 2) %>% -->

<!-- #     dfm_trim(min_termfreq = 0.50, termfreq_type = "quantile", max_termfreq = 0.99) -->

<!-- #  -->

<!-- # stm_doc <- convert(open_dfm1, to = "stm") -->

<!-- # model <- stm(stm_doc$documents, stm_doc$vocab, K = 10) -->

<!-- # save(model, file = "models/exp2_disapp") -->

<!-- load("models/exp2_disapp") -->

<!-- plot.STM(model, type = "summary", main = "Top Topics: Experiment 2, Respondents who disapprove") -->

<!-- ``` -->